Characterization and biological activity of selenium nanoparticles biosynthesized by <i>Yarrowia lipolytica</i>
Bibliographic record
Abstract
In this research, biogenic selenium nanoparticles were produced by the fungi Yarrowia lipolytica, and the biological activity of its nanoparticles was studied for the first time. The electron microscopy analyses showed the production of nanoparticles were intracellular and the resulting particles were extracted and characterized by XRD, zeta potential, FESEM, EDX, FTIR spectroscopy and DLS. These analyses showed amorphous spherical nanoparticles with an average size of 110 nm and a Zeta potential of -34.51 ± 2.41 mV. Signatures of lipids and proteins were present in the capping layer of biogenic selenium nanoparticles based on FTIR spectra. The antimicrobial properties of test strains showed that Serratia marcescens, Klebsiella pneumonia, Escherichia coli, Pseudomonas aeruginosa and Bacillus subtilis were inhibited at concentrations between 160 and 640 μg/mL, while the growth of Candida albicans was hindered by 80 μg/mL of biogenic selenium nanoparticles. At concentrations between 0.5 and 1.5 mg/mL of biogenic selenium nanoparticles inhibited up to 50% of biofilm formation of Klebsiella pneumonia, Acinetobacter baumannii, Staphylococcus aureus and Pseudomonas aeruginosa. Additionally, the concentration of 20-640 μg/mL of these bioSeNPs showed antioxidant activity. Evaluating the cytotoxicity of these nanoparticles on the HUVEC and HepG2 cell lines did not show any significant toxicity within MIC concentrations of SeNPs. This defines that Y. lipolytica synthesized SeNPs have strong potential to be exploited as antimicrobial agents against pathogens of WHO concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".